Effect of Population Characteristics on Head Injury Mortality
Bibliographic record
Abstract
We have analyzed predictors of mortality following closed head injury in a series of 1,031 consecutive patients with closed head injury admitted to hospital from January 1986 through December 1990. All patients were treated in a uniform manner and surgical intervention was performed as soon as possible in patients with intracranial mass lesions. Logistic analysis was used to identify patient and injury characteristics that were independent predictors of mortality within this patient group. Significant predictors were Glasgow Coma Score at admission (p = 0.0000), age (p = 0.0000), bilaterally unreactive pupils (p = 0.0000), presence of multiple systemic injuries (p = 0.0004), presence of an intracranial mass lesion (p = 0.0006), and presence of unilateral pupillary abnormalities (p = 0.0279). In an attempt to clarify the relationship between the incidence of these characteristics in series of severely head-injured patients reported during the last 2 decades and the mortality reported in those series, regression analysis was carried out comparing the mean age reported in the series, incidence of mass lesions, and reported mortality. Sixty-four percent of the variability in reported mortality rates could be accounted for by differences in mean age of the patients and mass lesion incidence (p = 0.0035). We conclude that apparent improvements in head injury mortality in the last 2 decades may be partly or wholly due to different population characteristics in the reported series. Multiple injuries appear to be important contributors to patient mortality, and in the interest of improved description of head injury populations, the Injury Severity Score should be reported with age, mass lesion incidence, and Glasgow Coma Score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".